RAPIDS Deployment Documentation

This repository contains the source for the RAPIDS Deployment Documentation. It explains how to install, configure, and operate RAPIDS across local systems, GPU clusters, and managed compute services.

The documentation includes:

• Installation paths for local workstations, custom containers, and Slurm-managed HPC clusters.
• Infrastructure-specific deployment instructions for major cloud providers, including virtual machines, managed Kubernetes, and machine learning services.
• Integration guidance for compute and application platforms such as Kubernetes, Kubeflow, Databricks, Snowflake, and Google Colab.
• Practical guides and end-to-end notebook examples covering distributed data processing, machine learning, optimization, and MLOps workflows.

Repository Layout

• source/cloud/ contains provider-specific infrastructure instructions for deploying NVIDIA RAPIDS on cloud platforms like AWS, Azure, Google Cloud, and IBM Cloud. These pages cover services such as virtual machines, managed Kubernetes, and hosted machine learning environments.
• source/platforms/ explains how to run NVIDIA RAPIDS on cloud platforms such as Kubernetes, Kubeflow, KServe, Databricks, Snowflake, Google Colab, Coiled, Modal, and NVIDIA AI Workbench.
• source/guides/ contains focused, cross-platform guidance for deployment topics such as custom CUDA containers, Multi-Instance GPU, InfiniBand, Dask scheduler sizing, Kubernetes worker placement, and image caching.
• source/examples/ contains end-to-end Jupyter notebook workflows and the supporting Python scripts, Dockerfiles, environment files, and Kubernetes manifests needed to run them.
• source/hpc.md covers running NVIDIA RAPIDS on Slurm-managed HPC clusters, including interactive and batch jobs, environment modules, and distributed workloads.
• source/local.md is the entry point for running NVIDIA RAPIDS on a workstation or server using conda, pip, Docker, or WSL2.
• source/custom-docker.md describes how to build smaller, tailored NVIDIA RAPIDS container images with only the required libraries using conda or pip.

Build Locally

The site is built with Sphinx and requires Python 3.12 or newer. Dependencies are managed with uv.

uv venv
uv sync --locked
uv run make dirhtml

The generated site is written to build/dirhtml. For live previews while editing, run:

uv run sphinx-autobuild -b dirhtml source build/html

Published Documentation

• Stable documentation is published from release tags.
• Nightly documentation is published from the main branch.

See CONTRIBUTING.md for instructions on building, writing, linting, and releasing.